Overview

Dataset statistics

Number of variables30
Number of observations206593
Missing cells0
Missing cells (%)0.0%
Duplicate rows11173
Duplicate rows (%)5.4%
Total size in memory47.3 MiB
Average record size in memory240.0 B

Variable types

Categorical11
Numeric19

Alerts

Dataset has 11173 (5.4%) duplicate rowsDuplicates
first_browser has a high cardinality: 52 distinct values High cardinality
days_account_created_booking is highly correlated with days_first_active_booking and 3 other fieldsHigh correlation
days_first_active_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
month_account_created is highly correlated with weekyear_account_created and 2 other fieldsHigh correlation
year_account_created is highly correlated with year_first_activeHigh correlation
dayweek_account_created is highly correlated with dayweek_first_activeHigh correlation
weekyear_account_created is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
month_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
year_first_active is highly correlated with year_account_createdHigh correlation
dayweek_first_active is highly correlated with dayweek_account_createdHigh correlation
weekyear_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with weekyear_first_bookingHigh correlation
year_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
dayweek_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
weekyear_first_booking is highly correlated with month_first_bookingHigh correlation
days_account_created_booking is highly correlated with days_first_active_booking and 3 other fieldsHigh correlation
days_first_active_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
month_account_created is highly correlated with weekyear_account_created and 2 other fieldsHigh correlation
year_account_created is highly correlated with year_first_activeHigh correlation
dayweek_account_created is highly correlated with dayweek_first_activeHigh correlation
weekyear_account_created is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
month_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
year_first_active is highly correlated with year_account_createdHigh correlation
dayweek_first_active is highly correlated with dayweek_account_createdHigh correlation
weekyear_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with weekyear_first_bookingHigh correlation
year_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
dayweek_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
weekyear_first_booking is highly correlated with month_first_bookingHigh correlation
days_account_created_booking is highly correlated with days_first_active_booking and 3 other fieldsHigh correlation
days_first_active_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
month_account_created is highly correlated with weekyear_account_created and 2 other fieldsHigh correlation
year_account_created is highly correlated with year_first_activeHigh correlation
dayweek_account_created is highly correlated with dayweek_first_activeHigh correlation
weekyear_account_created is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
month_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
year_first_active is highly correlated with year_account_createdHigh correlation
dayweek_first_active is highly correlated with dayweek_account_createdHigh correlation
weekyear_first_active is highly correlated with month_account_created and 2 other fieldsHigh correlation
day_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with weekyear_first_bookingHigh correlation
year_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
dayweek_first_booking is highly correlated with days_account_created_booking and 3 other fieldsHigh correlation
weekyear_first_booking is highly correlated with month_first_bookingHigh correlation
signup_app is highly correlated with first_device_type and 1 other fieldsHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
affiliate_provider is highly correlated with affiliate_channelHigh correlation
affiliate_channel is highly correlated with affiliate_providerHigh correlation
first_browser is highly correlated with signup_app and 1 other fieldsHigh correlation
gender is highly correlated with ageHigh correlation
age is highly correlated with genderHigh correlation
signup_flow is highly correlated with affiliate_channel and 4 other fieldsHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 1 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
country_destination is highly correlated with days_account_created_booking and 6 other fieldsHigh correlation
days_account_created_booking is highly correlated with country_destination and 12 other fieldsHigh correlation
days_first_active_booking is highly correlated with country_destination and 12 other fieldsHigh correlation
day_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with days_account_created_booking and 8 other fieldsHigh correlation
year_account_created is highly correlated with days_account_created_booking and 7 other fieldsHigh correlation
dayweek_account_created is highly correlated with dayweek_first_activeHigh correlation
weekyear_account_created is highly correlated with days_account_created_booking and 8 other fieldsHigh correlation
day_first_active is highly correlated with day_account_created and 1 other fieldsHigh correlation
month_first_active is highly correlated with days_account_created_booking and 8 other fieldsHigh correlation
year_first_active is highly correlated with days_account_created_booking and 7 other fieldsHigh correlation
dayweek_first_active is highly correlated with dayweek_account_createdHigh correlation
weekyear_first_active is highly correlated with days_account_created_booking and 8 other fieldsHigh correlation
day_first_booking is highly correlated with country_destination and 8 other fieldsHigh correlation
month_first_booking is highly correlated with country_destination and 10 other fieldsHigh correlation
year_first_booking is highly correlated with country_destination and 8 other fieldsHigh correlation
dayweek_first_booking is highly correlated with country_destination and 6 other fieldsHigh correlation
weekyear_first_booking is highly correlated with country_destination and 10 other fieldsHigh correlation
days_first_active_account_created is highly skewed (γ1 = 69.29642597) Skewed
signup_flow has 162557 (78.7%) zeros Zeros
days_account_created_booking has 20741 (10.0%) zeros Zeros
days_first_active_booking has 20738 (10.0%) zeros Zeros
days_first_active_account_created has 206421 (99.9%) zeros Zeros
dayweek_account_created has 31830 (15.4%) zeros Zeros
dayweek_first_active has 31837 (15.4%) zeros Zeros
dayweek_first_booking has 132217 (64.0%) zeros Zeros

Reproduction

Analysis started2022-03-24 17:47:33.663478
Analysis finished2022-03-24 17:48:27.307757
Duration53.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

gender
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
-unknown-
91706 
FEMALE
61520 
MALE
53092 
OTHER
 
275

Length

Max length9
Median length6
Mean length6.816382936
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-unknown-
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th row-unknown-

Common Values

ValueCountFrequency (%)
-unknown-91706
44.4%
FEMALE61520
29.8%
MALE53092
25.7%
OTHER275
 
0.1%

Length

2022-03-24T14:48:27.363740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:27.418731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown91706
44.4%
female61520
29.8%
male53092
25.7%
other275
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.24076324
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:27.484682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile24
Q132
median37
Q337
95-th percentile57
Maximum115
Range99
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.74199212
Coefficient of variation (CV)0.2884471528
Kurtosis13.18806297
Mean37.24076324
Median Absolute Deviation (MAD)3
Skewness2.756894938
Sum7693681
Variance115.3903948
MonotonicityNot monotonic
2022-03-24T14:48:27.569301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3787575
42.4%
306039
 
2.9%
315935
 
2.9%
295894
 
2.9%
285862
 
2.8%
325763
 
2.8%
275671
 
2.7%
335455
 
2.6%
264960
 
2.4%
344940
 
2.4%
Other values (89)68499
33.2%
ValueCountFrequency (%)
1626
 
< 0.1%
1764
 
< 0.1%
18665
 
0.3%
191097
 
0.5%
20533
 
0.3%
21969
 
0.5%
221679
 
0.8%
232424
1.2%
243173
1.5%
254405
2.1%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110188
 
0.1%
10931
 
< 0.1%
10815
 
< 0.1%
10723
 
< 0.1%
10617
 
< 0.1%
1051127
0.5%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
basic
147635 
facebook
58412 
google
 
546

Length

Max length8
Median length5
Mean length5.850861355
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowfacebook
3rd rowbasic
4th rowfacebook
5th rowbasic

Common Values

ValueCountFrequency (%)
basic147635
71.5%
facebook58412
 
28.3%
google546
 
0.3%

Length

2022-03-24T14:48:27.651876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:27.698782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
basic147635
71.5%
facebook58412
 
28.3%
google546
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.156946266
Minimum0
Maximum25
Zeros162557
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:27.740749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.550683626
Coefficient of variation (CV)2.39176818
Kurtosis3.551615681
Mean3.156946266
Median Absolute Deviation (MAD)0
Skewness2.283783603
Sum652203
Variance57.01282322
MonotonicityNot monotonic
2022-03-24T14:48:27.805949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0162557
78.7%
2513724
 
6.6%
128897
 
4.3%
37550
 
3.7%
25522
 
2.7%
243975
 
1.9%
232793
 
1.4%
1837
 
0.4%
6240
 
0.1%
8237
 
0.1%
Other values (7)261
 
0.1%
ValueCountFrequency (%)
0162557
78.7%
1837
 
0.4%
25522
 
2.7%
37550
 
3.7%
41
 
< 0.1%
531
 
< 0.1%
6240
 
0.1%
8237
 
0.1%
102
 
< 0.1%
128897
 
4.3%
ValueCountFrequency (%)
2513724
6.6%
243975
 
1.9%
232793
 
1.4%
21195
 
0.1%
2014
 
< 0.1%
169
 
< 0.1%
159
 
< 0.1%
128897
4.3%
102
 
< 0.1%
8237
 
0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
en
199636 
zh
 
1599
fr
 
1146
es
 
888
ko
 
720
Other values (20)
 
2604

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en199636
96.6%
zh1599
 
0.8%
fr1146
 
0.6%
es888
 
0.4%
ko720
 
0.3%
de715
 
0.3%
it489
 
0.2%
ru378
 
0.2%
pt234
 
0.1%
ja224
 
0.1%
Other values (15)564
 
0.3%

Length

2022-03-24T14:48:27.892967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en199636
96.6%
zh1599
 
0.8%
fr1146
 
0.6%
es888
 
0.4%
ko720
 
0.3%
de715
 
0.3%
it489
 
0.2%
ru378
 
0.2%
pt234
 
0.1%
ja224
 
0.1%
Other values (15)564
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
133678 
sem-brand
25681 
sem-non-brand
17949 
seo
 
8420
other
 
8296
Other values (3)
 
12569

Length

Max length13
Median length6
Mean length6.7501077
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowseo
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct133678
64.7%
sem-brand25681
 
12.4%
sem-non-brand17949
 
8.7%
seo8420
 
4.1%
other8296
 
4.0%
api7736
 
3.7%
content3780
 
1.8%
remarketing1053
 
0.5%

Length

2022-03-24T14:48:27.984240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:28.036485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
direct133678
64.7%
sem-brand25681
 
12.4%
sem-non-brand17949
 
8.7%
seo8420
 
4.1%
other8296
 
4.0%
api7736
 
3.7%
content3780
 
1.8%
remarketing1053
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
direct
133438 
google
50252 
other
 
11867
craigslist
 
2964
bing
 
2253
Other values (13)
 
5819

Length

Max length19
Median length6
Mean length6.035233527
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdirect
2nd rowgoogle
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct133438
64.6%
google50252
 
24.3%
other11867
 
5.7%
craigslist2964
 
1.4%
bing2253
 
1.1%
facebook2196
 
1.1%
padmapper766
 
0.4%
vast748
 
0.4%
facebook-open-graph545
 
0.3%
yahoo495
 
0.2%
Other values (8)1069
 
0.5%

Length

2022-03-24T14:48:28.116879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct133438
64.6%
google50252
 
24.3%
other11867
 
5.7%
craigslist2964
 
1.4%
bing2253
 
1.1%
facebook2196
 
1.1%
padmapper766
 
0.4%
vast748
 
0.4%
facebook-open-graph545
 
0.3%
yahoo495
 
0.2%
Other values (8)1069
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_affiliate_tracked
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
untracked
108838 
linked
46084 
omg
43830 
tracked-other
 
6123
product
 
1545
Other values (2)
 
173

Length

Max length13
Median length9
Mean length7.161457552
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked108838
52.7%
linked46084
22.3%
omg43830
21.2%
tracked-other6123
 
3.0%
product1545
 
0.7%
marketing139
 
0.1%
local ops34
 
< 0.1%

Length

2022-03-24T14:48:28.192457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:28.240129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
untracked108838
52.7%
linked46084
22.3%
omg43830
21.2%
tracked-other6123
 
3.0%
product1545
 
0.7%
marketing139
 
0.1%
local34
 
< 0.1%
ops34
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Web
177591 
iOS
17852 
Moweb
 
5771
Android
 
5379

Length

Max length7
Median length3
Mean length3.160015102
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web177591
86.0%
iOS17852
 
8.6%
Moweb5771
 
2.8%
Android5379
 
2.6%

Length

2022-03-24T14:48:28.318140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:28.372336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
web177591
86.0%
ios17852
 
8.6%
moweb5771
 
2.8%
android5379
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Mac Desktop
89255 
Windows Desktop
72410 
iPhone
20712 
iPad
14281 
Other/Unknown
 
4591
Other values (4)
 
5344

Length

Max length18
Median length11
Mean length11.53261727
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowMac Desktop
3rd rowWindows Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop89255
43.2%
Windows Desktop72410
35.0%
iPhone20712
 
10.0%
iPad14281
 
6.9%
Other/Unknown4591
 
2.2%
Android Phone2788
 
1.3%
Android Tablet1285
 
0.6%
Desktop (Other)1196
 
0.6%
SmartPhone (Other)75
 
< 0.1%

Length

2022-03-24T14:48:28.426263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:28.477724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
desktop162861
43.6%
mac89255
23.9%
windows72410
19.4%
iphone20712
 
5.5%
ipad14281
 
3.8%
other/unknown4591
 
1.2%
android4073
 
1.1%
phone2788
 
0.7%
tablet1285
 
0.3%
other1271
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

first_browser
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Chrome
63620 
Safari
44981 
Firefox
33513 
-unknown-
21166 
IE
20970 
Other values (47)
22343 

Length

Max length20
Median length6
Mean length6.808502708
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowChrome
3rd rowIE
4th rowFirefox
5th rowChrome

Common Values

ValueCountFrequency (%)
Chrome63620
30.8%
Safari44981
21.8%
Firefox33513
16.2%
-unknown-21166
 
10.2%
IE20970
 
10.2%
Mobile Safari19195
 
9.3%
Chrome Mobile1258
 
0.6%
Android Browser844
 
0.4%
AOL Explorer240
 
0.1%
Opera187
 
0.1%
Other values (42)619
 
0.3%

Length

2022-03-24T14:48:28.572544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome64878
28.4%
safari64176
28.1%
firefox33543
14.7%
unknown21166
 
9.3%
ie21006
 
9.2%
mobile20521
 
9.0%
browser907
 
0.4%
android844
 
0.4%
explorer273
 
0.1%
aol240
 
0.1%
Other values (48)799
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country_destination
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NDF
119810 
US
60800 
other
 
9935
FR
 
4881
IT
 
2776
Other values (7)
 
8391

Length

Max length5
Median length3
Mean length2.724201691
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowNDF
3rd rowUS
4th rowother
5th rowUS

Common Values

ValueCountFrequency (%)
NDF119810
58.0%
US60800
29.4%
other9935
 
4.8%
FR4881
 
2.4%
IT2776
 
1.3%
GB2285
 
1.1%
ES2203
 
1.1%
CA1385
 
0.7%
DE1033
 
0.5%
NL746
 
0.4%
Other values (2)739
 
0.4%

Length

2022-03-24T14:48:28.645378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf119810
58.0%
us60800
29.4%
other9935
 
4.8%
fr4881
 
2.4%
it2776
 
1.3%
gb2285
 
1.1%
es2203
 
1.1%
ca1385
 
0.7%
de1033
 
0.5%
nl746
 
0.4%
Other values (2)739
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

days_account_created_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1963
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.4993586
Minimum-349
Maximum2001
Zeros20741
Zeros (%)10.0%
Negative28
Negative (%)< 0.1%
Memory size1.6 MiB
2022-03-24T14:48:28.719949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q16
median416
Q3678
95-th percentile1108
Maximum2001
Range2350
Interquartile range (IQR)672

Descriptive statistics

Standard deviation388.117994
Coefficient of variation (CV)0.9208032848
Kurtosis-0.2323514341
Mean421.4993586
Median Absolute Deviation (MAD)374
Skewness0.6276303273
Sum87078817
Variance150635.5773
MonotonicityNot monotonic
2022-03-24T14:48:28.810938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020741
 
10.0%
114289
 
6.9%
26309
 
3.1%
33897
 
1.9%
42845
 
1.4%
52197
 
1.1%
61738
 
0.8%
71611
 
0.8%
81276
 
0.6%
91022
 
0.5%
Other values (1953)150668
72.9%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
-2951
< 0.1%
-2691
< 0.1%
-2611
< 0.1%
-2081
< 0.1%
-1671
< 0.1%
ValueCountFrequency (%)
20012
< 0.1%
19991
 
< 0.1%
19952
< 0.1%
19941
 
< 0.1%
19921
 
< 0.1%
19913
< 0.1%
19902
< 0.1%
19821
 
< 0.1%
19801
 
< 0.1%
19791
 
< 0.1%

days_first_active_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1940
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean421.7360414
Minimum0
Maximum2293
Zeros20738
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:28.913438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median416
Q3678
95-th percentile1108
Maximum2293
Range2293
Interquartile range (IQR)672

Descriptive statistics

Standard deviation388.2299321
Coefficient of variation (CV)0.9205519424
Kurtosis-0.2279504451
Mean421.7360414
Median Absolute Deviation (MAD)374
Skewness0.6286949255
Sum87127714
Variance150722.4802
MonotonicityNot monotonic
2022-03-24T14:48:29.007131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020738
 
10.0%
114288
 
6.9%
26307
 
3.1%
33894
 
1.9%
42845
 
1.4%
52193
 
1.1%
61735
 
0.8%
71611
 
0.8%
81275
 
0.6%
91024
 
0.5%
Other values (1930)150683
72.9%
ValueCountFrequency (%)
020738
10.0%
114288
6.9%
26307
 
3.1%
33894
 
1.9%
42845
 
1.4%
52193
 
1.1%
61735
 
0.8%
71611
 
0.8%
81275
 
0.6%
91024
 
0.5%
ValueCountFrequency (%)
22931
 
< 0.1%
22281
 
< 0.1%
20012
< 0.1%
19991
 
< 0.1%
19952
< 0.1%
19941
 
< 0.1%
19921
 
< 0.1%
19913
< 0.1%
19902
< 0.1%
19821
 
< 0.1%

days_first_active_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct142
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.236682753
Minimum0
Maximum1456
Zeros206421
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.097848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.1122496
Coefficient of variation (CV)51.17504104
Kurtosis5699.565643
Mean0.236682753
Median Absolute Deviation (MAD)0
Skewness69.29642597
Sum48897
Variance146.7065904
MonotonicityNot monotonic
2022-03-24T14:48:29.185021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0206421
99.9%
16
 
< 0.1%
64
 
< 0.1%
53
 
< 0.1%
293
 
< 0.1%
23
 
< 0.1%
73
 
< 0.1%
33
 
< 0.1%
1032
 
< 0.1%
952
 
< 0.1%
Other values (132)143
 
0.1%
ValueCountFrequency (%)
0206421
99.9%
16
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
42
 
< 0.1%
53
 
< 0.1%
64
 
< 0.1%
73
 
< 0.1%
92
 
< 0.1%
101
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%
10181
< 0.1%
10111
< 0.1%
9981
< 0.1%
9951
< 0.1%
8821
< 0.1%

day_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8729531
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.267408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.742507746
Coefficient of variation (CV)0.5507801661
Kurtosis-1.187560979
Mean15.8729531
Median Absolute Deviation (MAD)8
Skewness-0.01138779605
Sum3279241
Variance76.43144168
MonotonicityNot monotonic
2022-03-24T14:48:29.340876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247183
 
3.5%
207018
 
3.4%
187009
 
3.4%
166994
 
3.4%
236990
 
3.4%
196955
 
3.4%
286921
 
3.4%
176904
 
3.3%
266904
 
3.3%
136883
 
3.3%
Other values (21)136832
66.2%
ValueCountFrequency (%)
15969
2.9%
26564
3.2%
36754
3.3%
46620
3.2%
56817
3.3%
66770
3.3%
76506
3.1%
86701
3.2%
96713
3.2%
106808
3.3%
ValueCountFrequency (%)
313605
1.7%
306588
3.2%
296366
3.1%
286921
3.4%
276841
3.3%
266904
3.3%
256724
3.3%
247183
3.5%
236990
3.4%
226725
3.3%

month_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.016994767
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.413804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.221653778
Coefficient of variation (CV)0.5354257238
Kurtosis-0.9530348208
Mean6.016994767
Median Absolute Deviation (MAD)3
Skewness0.252885905
Sum1243069
Variance10.37905307
MonotonicityNot monotonic
2022-03-24T14:48:29.476191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627028
13.1%
525531
12.4%
421324
10.3%
319481
9.4%
116773
8.1%
215855
7.7%
914780
7.2%
814060
6.8%
713405
6.5%
1013030
6.3%
Other values (2)25326
12.3%
ValueCountFrequency (%)
116773
8.1%
215855
7.7%
319481
9.4%
421324
10.3%
525531
12.4%
627028
13.1%
713405
6.5%
814060
6.8%
914780
7.2%
1013030
6.3%
ValueCountFrequency (%)
1212803
6.2%
1112523
6.1%
1013030
6.3%
914780
7.2%
814060
6.8%
713405
6.5%
627028
13.1%
525531
12.4%
421324
10.3%
319481
9.4%

year_account_created
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2013
81851 
2014
75532 
2012
37936 
2011
9313 
2010
 
1961

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2011
3rd row2010
4th row2011
5th row2010

Common Values

ValueCountFrequency (%)
201381851
39.6%
201475532
36.6%
201237936
18.4%
20119313
 
4.5%
20101961
 
0.9%

Length

2022-03-24T14:48:29.550754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-24T14:48:29.615509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
201381851
39.6%
201475532
36.6%
201237936
18.4%
20119313
 
4.5%
20101961
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dayweek_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.762199106
Minimum0
Maximum6
Zeros31830
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.664862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944268962
Coefficient of variation (CV)0.7038844367
Kurtosis-1.1498462
Mean2.762199106
Median Absolute Deviation (MAD)2
Skewness0.1677017806
Sum570651
Variance3.780181796
MonotonicityNot monotonic
2022-03-24T14:48:29.719501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
133992
16.5%
233040
16.0%
031830
15.4%
331499
15.2%
428811
13.9%
623733
11.5%
523688
11.5%
ValueCountFrequency (%)
031830
15.4%
133992
16.5%
233040
16.0%
331499
15.2%
428811
13.9%
523688
11.5%
623733
11.5%
ValueCountFrequency (%)
623733
11.5%
523688
11.5%
428811
13.9%
331499
15.2%
233040
16.0%
133992
16.5%
031830
15.4%

weekyear_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37418015
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.795698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.95489097
Coefficient of variation (CV)0.5725276042
Kurtosis-0.9413055266
Mean24.37418015
Median Absolute Deviation (MAD)11
Skewness0.2533569758
Sum5035535
Variance194.7389819
MonotonicityNot monotonic
2022-03-24T14:48:29.900759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266777
 
3.3%
256426
 
3.1%
246164
 
3.0%
216116
 
3.0%
236061
 
2.9%
206057
 
2.9%
225606
 
2.7%
195486
 
2.7%
185441
 
2.6%
175308
 
2.6%
Other values (43)147151
71.2%
ValueCountFrequency (%)
13198
1.5%
23826
1.9%
34025
1.9%
43785
1.8%
53795
1.8%
63913
1.9%
73844
1.9%
84038
2.0%
94282
2.1%
104236
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
522671
1.3%
512785
1.3%
502891
1.4%
493173
1.5%
482806
1.4%
472893
1.4%
463063
1.5%
453068
1.5%
442745
1.3%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.8726772
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:29.995881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.74204279
Coefficient of variation (CV)0.5507604471
Kurtosis-1.187475222
Mean15.8726772
Median Absolute Deviation (MAD)8
Skewness-0.01122201687
Sum3279184
Variance76.42331214
MonotonicityNot monotonic
2022-03-24T14:48:30.069215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
247182
 
3.5%
207014
 
3.4%
187010
 
3.4%
166988
 
3.4%
236988
 
3.4%
196956
 
3.4%
286915
 
3.3%
176908
 
3.3%
266908
 
3.3%
136889
 
3.3%
Other values (21)136835
66.2%
ValueCountFrequency (%)
15967
2.9%
26561
3.2%
36750
3.3%
46620
3.2%
56817
3.3%
66772
3.3%
76510
3.2%
86698
3.2%
96717
3.3%
106810
3.3%
ValueCountFrequency (%)
313607
1.7%
306587
3.2%
296363
3.1%
286915
3.3%
276842
3.3%
266908
3.3%
256728
3.3%
247182
3.5%
236988
3.4%
226720
3.3%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.016956044
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:30.140277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.221454147
Coefficient of variation (CV)0.5353959915
Kurtosis-0.9528826524
Mean6.016956044
Median Absolute Deviation (MAD)3
Skewness0.2529472005
Sum1243061
Variance10.37776682
MonotonicityNot monotonic
2022-03-24T14:48:30.199893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
627033
13.1%
525525
12.4%
421333
10.3%
319482
9.4%
116768
8.1%
215853
7.7%
914774
7.2%
814061
6.8%
713410
6.5%
1013031
6.3%
Other values (2)25323
12.3%
ValueCountFrequency (%)
116768
8.1%
215853
7.7%
319482
9.4%
421333
10.3%
525525
12.4%
627033
13.1%
713410
6.5%
814061
6.8%
914774
7.2%
1013031
6.3%
ValueCountFrequency (%)
1212799
6.2%
1112524
6.1%
1013031
6.3%
914774
7.2%
814061
6.8%
713410
6.5%
627033
13.1%
525525
12.4%
421333
10.3%
319482
9.4%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.062703
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:30.256472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12013
median2013
Q32014
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9011508238
Coefficient of variation (CV)0.0004476516417
Kurtosis0.3051540033
Mean2013.062703
Median Absolute Deviation (MAD)1
Skewness-0.8084367444
Sum415884663
Variance0.8120728072
MonotonicityIncreasing
2022-03-24T14:48:30.316738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201381841
39.6%
201475496
36.5%
201237950
18.4%
20119331
 
4.5%
20101970
 
1.0%
20095
 
< 0.1%
ValueCountFrequency (%)
20095
 
< 0.1%
20101970
 
1.0%
20119331
 
4.5%
201237950
18.4%
201381841
39.6%
201475496
36.5%
ValueCountFrequency (%)
201475496
36.5%
201381841
39.6%
201237950
18.4%
20119331
 
4.5%
20101970
 
1.0%
20095
 
< 0.1%

dayweek_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.762053893
Minimum0
Maximum6
Zeros31837
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:30.379905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.944268623
Coefficient of variation (CV)0.7039213201
Kurtosis-1.149772853
Mean2.762053893
Median Absolute Deviation (MAD)2
Skewness0.1677710956
Sum570621
Variance3.780180478
MonotonicityNot monotonic
2022-03-24T14:48:30.440869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
133988
16.5%
233041
16.0%
031837
15.4%
331504
15.2%
428807
13.9%
623731
11.5%
523685
11.5%
ValueCountFrequency (%)
031837
15.4%
133988
16.5%
233041
16.0%
331504
15.2%
428807
13.9%
523685
11.5%
623731
11.5%
ValueCountFrequency (%)
623731
11.5%
523685
11.5%
428807
13.9%
331504
15.2%
233041
16.0%
133988
16.5%
031837
15.4%

weekyear_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.37388973
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:30.520806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median23
Q336
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.95400971
Coefficient of variation (CV)0.5724982704
Kurtosis-0.9411483504
Mean24.37388973
Median Absolute Deviation (MAD)11
Skewness0.2534181308
Sum5035475
Variance194.714387
MonotonicityNot monotonic
2022-03-24T14:48:30.623578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266779
 
3.3%
256426
 
3.1%
246164
 
3.0%
216116
 
3.0%
236062
 
2.9%
206057
 
2.9%
225602
 
2.7%
195490
 
2.7%
185433
 
2.6%
175309
 
2.6%
Other values (43)147155
71.2%
ValueCountFrequency (%)
13196
1.5%
23824
1.9%
34026
1.9%
43785
1.8%
53794
1.8%
63913
1.9%
73842
1.9%
84038
2.0%
94281
2.1%
104236
2.1%
ValueCountFrequency (%)
533
 
< 0.1%
522671
1.3%
512784
1.3%
502890
1.4%
493170
1.5%
482807
1.4%
472890
1.4%
463068
1.5%
453066
1.5%
442745
1.3%

day_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.39231242
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:31.124291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median29
Q329
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.68258139
Coefficient of variation (CV)0.3711724277
Kurtosis0.1355314098
Mean23.39231242
Median Absolute Deviation (MAD)0
Skewness-1.2661289
Sum4832688
Variance75.3872196
MonotonicityNot monotonic
2022-03-24T14:48:31.194829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
29122367
59.2%
103009
 
1.5%
172996
 
1.5%
112982
 
1.4%
162964
 
1.4%
132950
 
1.4%
152950
 
1.4%
52918
 
1.4%
122892
 
1.4%
32882
 
1.4%
Other values (21)57683
27.9%
ValueCountFrequency (%)
12690
1.3%
22807
1.4%
32882
1.4%
42784
1.3%
52918
1.4%
62876
1.4%
72863
1.4%
82882
1.4%
92848
1.4%
103009
1.5%
ValueCountFrequency (%)
311526
 
0.7%
302650
 
1.3%
29122367
59.2%
282809
 
1.4%
272698
 
1.3%
262757
 
1.3%
252805
 
1.4%
242824
 
1.4%
232793
 
1.4%
222860
 
1.4%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.043592958
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:31.263150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median6
Q36
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.059181648
Coefficient of variation (CV)0.3407214321
Kurtosis1.874039975
Mean6.043592958
Median Absolute Deviation (MAD)0
Skewness0.385503438
Sum1248564
Variance4.240229059
MonotonicityNot monotonic
2022-03-24T14:48:31.320850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6130148
63.0%
510322
 
5.0%
48624
 
4.2%
38159
 
3.9%
77073
 
3.4%
86854
 
3.3%
26616
 
3.2%
96402
 
3.1%
16338
 
3.1%
106009
 
2.9%
Other values (2)10048
 
4.9%
ValueCountFrequency (%)
16338
 
3.1%
26616
 
3.2%
38159
 
3.9%
48624
 
4.2%
510322
 
5.0%
6130148
63.0%
77073
 
3.4%
86854
 
3.3%
96402
 
3.1%
106009
 
2.9%
ValueCountFrequency (%)
124944
 
2.4%
115104
 
2.5%
106009
 
2.9%
96402
 
3.1%
86854
 
3.3%
77073
 
3.4%
6130148
63.0%
510322
 
5.0%
48624
 
4.2%
38159
 
3.9%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.195563
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:31.377822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012
Q12013
median2015
Q32015
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1387295
Coefficient of variation (CV)0.0005653520048
Kurtosis0.7825344175
Mean2014.195563
Median Absolute Deviation (MAD)0
Skewness-1.285470714
Sum416118704
Variance1.296704874
MonotonicityNot monotonic
2022-03-24T14:48:31.436365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2015121581
58.9%
201432334
 
15.7%
201331083
 
15.0%
201215797
 
7.6%
20114690
 
2.3%
20101108
 
0.5%
ValueCountFrequency (%)
20101108
 
0.5%
20114690
 
2.3%
201215797
 
7.6%
201331083
 
15.0%
201432334
 
15.7%
2015121581
58.9%
ValueCountFrequency (%)
2015121581
58.9%
201432334
 
15.7%
201331083
 
15.0%
201215797
 
7.6%
20114690
 
2.3%
20101108
 
0.5%

dayweek_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.177590722
Minimum0
Maximum6
Zeros132217
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:31.493771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.858781277
Coefficient of variation (CV)1.578461211
Kurtosis0.4860784427
Mean1.177590722
Median Absolute Deviation (MAD)0
Skewness1.364758733
Sum243282
Variance3.455067834
MonotonicityNot monotonic
2022-03-24T14:48:31.548273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0132217
64.0%
214029
 
6.8%
113970
 
6.8%
313627
 
6.6%
412972
 
6.3%
510183
 
4.9%
69595
 
4.6%
ValueCountFrequency (%)
0132217
64.0%
113970
 
6.8%
214029
 
6.8%
313627
 
6.6%
412972
 
6.3%
510183
 
4.9%
69595
 
4.6%
ValueCountFrequency (%)
69595
 
4.6%
510183
 
4.9%
412972
 
6.3%
313627
 
6.6%
214029
 
6.8%
113970
 
6.8%
0132217
64.0%

weekyear_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.04670536
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2022-03-24T14:48:31.622006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q127
median27
Q327
95-th percentile44
Maximum53
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.986604103
Coefficient of variation (CV)0.3450188413
Kurtosis1.663691743
Mean26.04670536
Median Absolute Deviation (MAD)0
Skewness-0.1377309308
Sum5381067
Variance80.7590533
MonotonicityNot monotonic
2022-03-24T14:48:31.709756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27121629
58.9%
262488
 
1.2%
242462
 
1.2%
212451
 
1.2%
202434
 
1.2%
252403
 
1.2%
232366
 
1.1%
182277
 
1.1%
192263
 
1.1%
222218
 
1.1%
Other values (43)63602
30.8%
ValueCountFrequency (%)
11092
0.5%
21438
0.7%
31717
0.8%
41401
0.7%
51444
0.7%
61590
0.8%
71719
0.8%
81672
0.8%
91748
0.8%
101840
0.9%
ValueCountFrequency (%)
531
 
< 0.1%
52915
0.4%
511130
0.5%
501172
0.6%
491249
0.6%
481121
0.5%
471118
0.5%
461191
0.6%
451354
0.7%
441188
0.6%

Interactions

2022-03-24T14:48:23.323399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:47.012891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:49.121970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:51.035066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:53.156111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:55.047872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.978988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:59.029564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.909991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.821153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.967436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.851173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.743462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.954735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.930611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.900599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.896311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.901260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:21.279166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:23.432622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:47.151950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:49.219428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:51.134212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:53.251112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:55.146412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:57.243297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:59.128028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:01.003878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.914755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:05.068138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.941307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.837986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:11.053586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:13.026064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.999000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.994294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:19.002360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:21.377542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:23.548407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:47.265946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:49.316135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:51.236788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:53.355457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:55.246256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:57.339453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-24T14:48:22.536299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:24.815372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:48.512052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.433922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:52.560294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.454481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.366048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.414182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.308758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.215232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.330673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.260829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.156931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.066490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.253675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.299145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.264985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.270196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:20.666410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:22.646634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:24.920987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:48.618114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.530113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:52.657446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.551429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.464228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.514372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.407567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.319814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.441619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.356457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.252795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.170162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.357765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.396362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.377671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.368430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:20.765356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:22.748764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:25.037356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:48.719657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.630045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:52.755707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.651117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.566118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.614920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.507683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.419918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.552058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.453801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.351847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.281058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.461085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.492780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.478972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.468343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:20.865509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:22.868018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:25.146095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:48.819893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.730635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:52.853473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.751052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.667220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.714920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.608306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.519418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.666035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.551178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.448007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.388164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.576169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.589240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.579566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.576646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:20.967767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:22.995698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:25.253631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:48.922136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.832122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:52.954036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.848541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.774408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.817426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.713308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.623384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.767250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.653389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.546001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.489435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.693889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.695944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.678996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.689894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:21.072307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:23.112197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:25.353842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:49.023982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:50.934667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:53.055231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:54.951729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:56.877353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:47:58.924016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:00.815389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:02.723173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:04.865658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:06.753855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:08.645828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:10.594532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:12.815571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:14.799368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:16.785481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:18.792350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:21.180667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-24T14:48:23.219290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-24T14:48:31.794895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-24T14:48:31.962124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-24T14:48:32.118081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-24T14:48:32.265013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-24T14:48:32.386715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-24T14:48:25.577389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-24T14:48:26.508928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

genderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_account_created_bookingdays_first_active_bookingdays_first_active_account_createdday_account_createdmonth_account_createdyear_account_createddayweek_account_createdweekyear_account_createdday_first_activemonth_first_activeyear_first_activedayweek_first_activeweekyear_first_activeday_first_bookingmonth_first_bookingyear_first_bookingdayweek_first_bookingweekyear_first_booking
0-unknown-37facebook0endirectdirectuntrackedWebMac DesktopChromeNDF18272293466286201002619320093122962015027
1MALE38facebook0enseogoogleuntrackedWebMac DesktopChromeNDF14962228732255201122123520095212962015027
2FEMALE56basic3endirectdirectuntrackedWebWindows DesktopIEUS-574194762892010139962009124282010031
3FEMALE42facebook0endirectdirectuntrackedWebMac DesktopFirefoxother2781043765512201104931102009544892012536
4-unknown-41basic0endirectdirectuntrackedWebMac DesktopChromeUS-2087228014920101378122009150182201037
5-unknown-37basic0enotherotheromgWebMac DesktopChromeUS110112010453112010453212010553
6FEMALE46basic0enothercraigslistuntrackedWebMac DesktopSafariUS33021201055321201055351201011
7FEMALE47basic0endirectdirectomgWebMac DesktopSafariUS10100312010653312010653131201022
8FEMALE50basic0enothercraigslistuntrackedWebMac DesktopSafariUS206206041201001412010012972010330
9-unknown-46basic0enothercraigslistomgWebMac DesktopFirefoxUS000412010014120100141201001

Last rows

genderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_account_created_bookingdays_first_active_bookingdays_first_active_account_createdday_account_createdmonth_account_createdyear_account_createddayweek_account_createdweekyear_account_createdday_first_activemonth_first_activeyear_first_activedayweek_first_activeweekyear_first_activeday_first_bookingmonth_first_bookingyear_first_bookingdayweek_first_bookingweekyear_first_booking
206583FEMALE34basic0endirectdirectlinkedWebMac DesktopChromeES44440306201402730620140271382014233
206584-unknown-37basic0ensem-brandgoogleomgWebMac DesktopChromeNDF3643640306201402730620140272962015027
206585FEMALE36basic0ensem-brandgooglelinkedWebMac DesktopSafariUS13130306201402730620140271372014628
206586-unknown-37basic0endirectdirectlinkedWebWindows DesktopChromeNDF3643640306201402730620140272962015027
206587FEMALE23basic0ensem-brandgoogleomgWebWindows DesktopIEUS22030620140273062014027272014227
206588MALE32basic0ensem-brandgoogleomgWebMac DesktopSafariNDF3643640306201402730620140272962015027
206589-unknown-37basic0endirectdirectlinkedWebWindows DesktopChromeNDF3643640306201402730620140272962015027
206590-unknown-32basic0endirectdirectuntrackedWebMac DesktopFirefoxNDF3643640306201402730620140272962015027
206591-unknown-37basic25enotherothertracked-otheriOSiPhoneMobile SafariNDF3643640306201402730620140272962015027
206592-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF3643640306201402730620140272962015027

Duplicate rows

Most frequently occurring

genderagesignup_methodsignup_flowlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destinationdays_account_created_bookingdays_first_active_bookingdays_first_active_account_createdday_account_createdmonth_account_createdyear_account_createddayweek_account_createdweekyear_account_createdday_first_activemonth_first_activeyear_first_activedayweek_first_activeweekyear_first_activeday_first_bookingmonth_first_bookingyear_first_bookingdayweek_first_bookingweekyear_first_booking# duplicates
9752-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF405405020520141212052014121296201502756
9739-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF3923920262014023262014023296201502750
9758-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF411411014520142201452014220296201502748
9759-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF412412013520141201352014120296201502748
9774-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF427427028420140182842014018296201502743
9738-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF3913910362014123362014123296201502740
9779-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF432432023420142172342014217296201502740
9711-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF364364030620140273062014027296201502738
9719-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF372372022620146252262014625296201502738
9749-unknown-37basic25endirectdirectuntrackediOSiPhone-unknown-NDF402402023520144212352014421296201502738